Pub Date : 2023-05-18DOI: 10.1177/14780771231177514
Likai Wang, Ting Luo, Tong Shao, Guohua Ji
In building massing design, using passive design strategies is a critical approach to reducing energy consumption while offering comfortable indoor environments. However, it is often impractical for architects to systematically explore passive design strategies at the outset of the building massing design and architectural form-finding processes, which may result in inefficient or ineffective utilization of the strategies. To address this issue, this study presents a reverse passive design strategy exploration approach that leverages the capability of computational optimization and parametric modeling to help architects identify feasible passive design strategies for building massing design. The approach is achieved using a building massing design generation and optimization tool, called EvoMass, and various building performance simulation tools in Rhino-Grasshopper. The optimization can produce site-specific design references that reflect rich performance implications associated with passive design strategies, such as atriums and self-shading. As such, architects can screen out promising passive design strategies corresponding to different performance factors from the optimization result. Two case studies related to daylighting, sky exposure, and solar heat utility are presented to demonstrate the approach, and the relevant utility and limitations are discussed.
{"title":"Reverse passive strategy exploration for building massing design-An optimization-aided approach","authors":"Likai Wang, Ting Luo, Tong Shao, Guohua Ji","doi":"10.1177/14780771231177514","DOIUrl":"https://doi.org/10.1177/14780771231177514","url":null,"abstract":"In building massing design, using passive design strategies is a critical approach to reducing energy consumption while offering comfortable indoor environments. However, it is often impractical for architects to systematically explore passive design strategies at the outset of the building massing design and architectural form-finding processes, which may result in inefficient or ineffective utilization of the strategies. To address this issue, this study presents a reverse passive design strategy exploration approach that leverages the capability of computational optimization and parametric modeling to help architects identify feasible passive design strategies for building massing design. The approach is achieved using a building massing design generation and optimization tool, called EvoMass, and various building performance simulation tools in Rhino-Grasshopper. The optimization can produce site-specific design references that reflect rich performance implications associated with passive design strategies, such as atriums and self-shading. As such, architects can screen out promising passive design strategies corresponding to different performance factors from the optimization result. Two case studies related to daylighting, sky exposure, and solar heat utility are presented to demonstrate the approach, and the relevant utility and limitations are discussed.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"445 - 461"},"PeriodicalIF":1.7,"publicationDate":"2023-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42682690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-17DOI: 10.1177/14780771231174526
P. Bedarf, A. Szabo, Enrico Scoccimarro, B. Dillenburger
This paper presents an innovative design and fabrication workflow for a lightweight composite slab prototype that combines mineral foam 3D printing (F3DP) and concrete casting. Non-standardized concrete elements that are geometrically optimized for resource efficiency often result in complex shapes that are difficult to manufacture. This paper extends the research in earlier studies, showing that F3DP can address this challenge. F3DP is used to construct 24 stay-in-place formwork elements for a lightweight, resource-efficient ribbed concrete element with a 2 × 1.3 m footprint. This advancement highlights the improved robotic F3DP setup, computational design techniques for geometry and print path generation, and strategies to achieve near-net-shape fabrication. The resulting prototype shows how complex geometries that were previously cost-prohibitive can be produced efficiently. Discussing the findings, challenges, and future improvements offers useful perspectives and supports the development of this resourceful and sustainable construction technique.
{"title":"Foamwork: Challenges and strategies in using mineral foam 3D printing for a lightweight composite concrete slab","authors":"P. Bedarf, A. Szabo, Enrico Scoccimarro, B. Dillenburger","doi":"10.1177/14780771231174526","DOIUrl":"https://doi.org/10.1177/14780771231174526","url":null,"abstract":"This paper presents an innovative design and fabrication workflow for a lightweight composite slab prototype that combines mineral foam 3D printing (F3DP) and concrete casting. Non-standardized concrete elements that are geometrically optimized for resource efficiency often result in complex shapes that are difficult to manufacture. This paper extends the research in earlier studies, showing that F3DP can address this challenge. F3DP is used to construct 24 stay-in-place formwork elements for a lightweight, resource-efficient ribbed concrete element with a 2 × 1.3 m footprint. This advancement highlights the improved robotic F3DP setup, computational design techniques for geometry and print path generation, and strategies to achieve near-net-shape fabrication. The resulting prototype shows how complex geometries that were previously cost-prohibitive can be produced efficiently. Discussing the findings, challenges, and future improvements offers useful perspectives and supports the development of this resourceful and sustainable construction technique.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"388 - 403"},"PeriodicalIF":1.7,"publicationDate":"2023-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41532160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-12DOI: 10.1177/14780771231170272
Andrew Kudless
This paper examines the prevalence of bias in artificial intelligence text-to-image models utilized in the architecture and design disciplines. The rapid pace of advancements in machine learning technologies, particularly in text-to-image generators, has significantly increased over the past year, making these tools more accessible to the design community. Accordingly, this paper aims to critically document and analyze the collective, computational, and cognitive biases that designers may encounter when working with these tools at this time. The paper delves into three hierarchical levels of operation and investigates the possible biases present at each level. Starting with the training data for large language models (LLM), the paper explores how these models may create biases privileging English-language users and perspectives. The paper subsequently investigates the digital materiality of models and how their weights generate specific aesthetic results. Finally, the report concludes by examining user biases through their prompt and image selections and the potential for platforms to perpetuate these biases through the application of user data during training. Graphical Abstract
{"title":"Hierarchies of bias in artificial intelligence architecture: Collective, computational, and cognitive","authors":"Andrew Kudless","doi":"10.1177/14780771231170272","DOIUrl":"https://doi.org/10.1177/14780771231170272","url":null,"abstract":"This paper examines the prevalence of bias in artificial intelligence text-to-image models utilized in the architecture and design disciplines. The rapid pace of advancements in machine learning technologies, particularly in text-to-image generators, has significantly increased over the past year, making these tools more accessible to the design community. Accordingly, this paper aims to critically document and analyze the collective, computational, and cognitive biases that designers may encounter when working with these tools at this time. The paper delves into three hierarchical levels of operation and investigates the possible biases present at each level. Starting with the training data for large language models (LLM), the paper explores how these models may create biases privileging English-language users and perspectives. The paper subsequently investigates the digital materiality of models and how their weights generate specific aesthetic results. Finally, the report concludes by examining user biases through their prompt and image selections and the potential for platforms to perpetuate these biases through the application of user data during training. Graphical Abstract","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"256 - 279"},"PeriodicalIF":1.7,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66024808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-10DOI: 10.1177/14780771231171939
Dana Cupkova, A. Wit, Matias del Campo, Mollie Claypool
In recent years, the field of architectural research has trended towards rapid evolution as new digital technologies that integrate artificial intelligence (AI) into design, representation, and production have become more prominent. As with any paradigm shift and rapid emergence of transformative technology, new tensions and fears of human distancing away from acts of design and making arise. Outside of architecture, AI already plays a significant role in fields such as engineering, IT, and the social/political sciences, with a deepening discourse on its effect on humanity, and the ethics of its labor. Architects must develop critical metrics, understand implicit biases, and probe new methodologies to better understand the impacts and implications these transformative technologies have within their own territory. It is now more urgent than ever for architecture to take a stance on shaping the agency of AI frameworks within the discipline. Traditionally, advances in architectural technologies were limited in access due to the high monetary costs and steep learning curves in the physical infrastructure and tools utilized in digital fabrication and robotic production. However, recent breakthroughs in AI technologies have seemed to enable the digital networks provided by AI to be increasingly distributed to those already abled by technological access. As a result of this paradigm shift, new models of economy and labor arise, and the use of AI yet again opens questions surrounding the role of authorship, ownership of data, and models of collaboration within the discipline. In this new era of increased AI ubiquity and seemingly rapid design freedom aided by machine learning (ML) frameworks, a series of critical questions emerge through the articles curated in this volume:
{"title":"AI, architecture, accessibility, and data justice—ACADIA special issue","authors":"Dana Cupkova, A. Wit, Matias del Campo, Mollie Claypool","doi":"10.1177/14780771231171939","DOIUrl":"https://doi.org/10.1177/14780771231171939","url":null,"abstract":"In recent years, the field of architectural research has trended towards rapid evolution as new digital technologies that integrate artificial intelligence (AI) into design, representation, and production have become more prominent. As with any paradigm shift and rapid emergence of transformative technology, new tensions and fears of human distancing away from acts of design and making arise. Outside of architecture, AI already plays a significant role in fields such as engineering, IT, and the social/political sciences, with a deepening discourse on its effect on humanity, and the ethics of its labor. Architects must develop critical metrics, understand implicit biases, and probe new methodologies to better understand the impacts and implications these transformative technologies have within their own territory. It is now more urgent than ever for architecture to take a stance on shaping the agency of AI frameworks within the discipline. Traditionally, advances in architectural technologies were limited in access due to the high monetary costs and steep learning curves in the physical infrastructure and tools utilized in digital fabrication and robotic production. However, recent breakthroughs in AI technologies have seemed to enable the digital networks provided by AI to be increasingly distributed to those already abled by technological access. As a result of this paradigm shift, new models of economy and labor arise, and the use of AI yet again opens questions surrounding the role of authorship, ownership of data, and models of collaboration within the discipline. In this new era of increased AI ubiquity and seemingly rapid design freedom aided by machine learning (ML) frameworks, a series of critical questions emerge through the articles curated in this volume:","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"209 - 210"},"PeriodicalIF":1.7,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47860404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-10DOI: 10.1177/14780771231168231
Benjamin Ennemoser, Ingrid Mayrhofer-Hufnagl
The development of Generative Adversarial Networks (GANs) has accelerated the research of Artificial Intelligence (AI) in architecture as a generative tool. However, since their initial invention, many versions have been developed that only focus on 2D image datasets for training and images as output. The current state of 3DGAN research has yielded promising results. However, these contributions focus primarily on building mass, extrusion of 2D plans, or the overall shape of objects. In comparison, our newly developed 3DGAN approach, using fully spatial building datasets, demonstrates that unprecedented interconnections across different scales are possible resulting in unconventional spatial configurations. Unlike a traditional design process, based on analyzing only a few precedents (typology) according to the task, by collaborating with the machine we can draw on a significantly wider variety of buildings across multiple typologies. In addition, the dataset was extended beyond the scale of complete buildings and involved building components that define space. Thus, our results achieve a high spatial diversity. A detailed analysis of the results also revealed new hybrid architectural elements illustrating that the machine continued the interconnections of scale since elements were not explicitly part of the dataset, becoming a true design collaborator.
{"title":"Design across multi-scale datasets by developing a novel approach to 3DGANs","authors":"Benjamin Ennemoser, Ingrid Mayrhofer-Hufnagl","doi":"10.1177/14780771231168231","DOIUrl":"https://doi.org/10.1177/14780771231168231","url":null,"abstract":"The development of Generative Adversarial Networks (GANs) has accelerated the research of Artificial Intelligence (AI) in architecture as a generative tool. However, since their initial invention, many versions have been developed that only focus on 2D image datasets for training and images as output. The current state of 3DGAN research has yielded promising results. However, these contributions focus primarily on building mass, extrusion of 2D plans, or the overall shape of objects. In comparison, our newly developed 3DGAN approach, using fully spatial building datasets, demonstrates that unprecedented interconnections across different scales are possible resulting in unconventional spatial configurations. Unlike a traditional design process, based on analyzing only a few precedents (typology) according to the task, by collaborating with the machine we can draw on a significantly wider variety of buildings across multiple typologies. In addition, the dataset was extended beyond the scale of complete buildings and involved building components that define space. Thus, our results achieve a high spatial diversity. A detailed analysis of the results also revealed new hybrid architectural elements illustrating that the machine continued the interconnections of scale since elements were not explicitly part of the dataset, becoming a true design collaborator.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"358 - 373"},"PeriodicalIF":1.7,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45329119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-03DOI: 10.1177/14780771231174890
Melih Kamaoğlu
Humans have always sought to grasp nature’s working principles and apply acquired intelligence to artefacts since nature has always been the source of inspiration, solution and creativity. For this reason, there is a comprehensive interrelationship between the philosophy of nature and architecture. After Charles Darwin’s revolutionary work, living beings have started to be comprehended as changing, evolving and developing dynamic entities. Evolution theory has been accepted as the interpretive power of biology after several discussions and objections among scientists. In time, the working principles of evolutionary mechanisms have begun to be explained from genetic code to organism and environmental level. Afterwards, simulating nature’s evolutionary logic in the digital interface has become achievable with computational systems’ advancements. Ultimately, architects have begun to utilise evolutionary understanding in design theories and methodologies through computational procedures since the 1990s. Although several studies about technical and pragmatic elements of evolutionary tools in design, there is still little research on the historical, theoretical and philosophical foundations of evolutionary understanding in digital architecture. This paper fills this literature gap by critically reviewing the evolutionary understanding embedded in digital architecture theories and designs since the beginning of the 1990s. The original contribution is the proposed intellectual framework seeking to understand and conceptualise how evolutionary processes were defined in biology and philosophy, then represented through computational procedures, to be finally utilised by architectural designers. The network of references and concepts is deeply connected with the communication between natural processes and their computational simulations. For this reason, another original contribution is the utilisation of theoretical limits and operative principles of computation procedures to shed light on the limitations, shortcomings and potentials of design theories regarding their speculations on the relationship between natural and computational ontologies.
{"title":"The idea of evolution in digital architecture: Toward united ontologies?","authors":"Melih Kamaoğlu","doi":"10.1177/14780771231174890","DOIUrl":"https://doi.org/10.1177/14780771231174890","url":null,"abstract":"Humans have always sought to grasp nature’s working principles and apply acquired intelligence to artefacts since nature has always been the source of inspiration, solution and creativity. For this reason, there is a comprehensive interrelationship between the philosophy of nature and architecture. After Charles Darwin’s revolutionary work, living beings have started to be comprehended as changing, evolving and developing dynamic entities. Evolution theory has been accepted as the interpretive power of biology after several discussions and objections among scientists. In time, the working principles of evolutionary mechanisms have begun to be explained from genetic code to organism and environmental level. Afterwards, simulating nature’s evolutionary logic in the digital interface has become achievable with computational systems’ advancements. Ultimately, architects have begun to utilise evolutionary understanding in design theories and methodologies through computational procedures since the 1990s. Although several studies about technical and pragmatic elements of evolutionary tools in design, there is still little research on the historical, theoretical and philosophical foundations of evolutionary understanding in digital architecture. This paper fills this literature gap by critically reviewing the evolutionary understanding embedded in digital architecture theories and designs since the beginning of the 1990s. The original contribution is the proposed intellectual framework seeking to understand and conceptualise how evolutionary processes were defined in biology and philosophy, then represented through computational procedures, to be finally utilised by architectural designers. The network of references and concepts is deeply connected with the communication between natural processes and their computational simulations. For this reason, another original contribution is the utilisation of theoretical limits and operative principles of computation procedures to shed light on the limitations, shortcomings and potentials of design theories regarding their speculations on the relationship between natural and computational ontologies.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41877807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-26DOI: 10.1177/14780771231168229
Panagiota Pouliou, Anca-Simona Horvath, G. Palamas
The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design.
{"title":"Speculative hybrids: Investigating the generation of conceptual architectural forms through the use of 3D generative adversarial networks","authors":"Panagiota Pouliou, Anca-Simona Horvath, G. Palamas","doi":"10.1177/14780771231168229","DOIUrl":"https://doi.org/10.1177/14780771231168229","url":null,"abstract":"The process of architectural design aims at solving complex problems that have loosely defined formulations, no explicit basis for terminating the problem-solving activity, and where no ideal solution can be achieved. This means that design problems, as wicked problems, sit in a space between incompleteness and precision. Applying digital tools in general and artificial intelligence in particular to design problems will then mediate solution spaces between incompleteness and precision. In this paper, we present a study where we employed machine learning algorithms to generate conceptual architectural forms for site-specific regulations. We created an annotated dataset of single-family homes and used it to train a 3D Generative Adversarial Network that generated annotated point clouds complying with site constraints. Then, we presented the framework to 23 practitioners of architecture in an attempt to understand whether this framework could be a useful tool for early-stage design. We make a three-fold contribution: First, we share an annotated dataset of architecturally relevant 3D point clouds of single-family homes. Next, we present and share the code for a framework and the results from training the 3D generative neural network. Finally, we discuss machine learning and creative work, including how practitioners feel about the emergence of these tools as mediators between incompleteness and precision in architectural design.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"315 - 336"},"PeriodicalIF":1.7,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44069156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-18DOI: 10.1177/14780771231170271
Ultan Byrne
This paper recommends that critical attention towards machine learning should be focused on the ordering procedures at work in these models. More precisely, it draws attention to the central role of ‘latent spaces.’ The paper first explores ‘latent space’ through a series of analogies, and then briefly situates the concept in relation to a genealogy reaching back to developments in mathematical statistics at the turn to the 20th century.
{"title":"A Parochial Comment on Midjourney","authors":"Ultan Byrne","doi":"10.1177/14780771231170271","DOIUrl":"https://doi.org/10.1177/14780771231170271","url":null,"abstract":"This paper recommends that critical attention towards machine learning should be focused on the ordering procedures at work in these models. More precisely, it draws attention to the central role of ‘latent spaces.’ The paper first explores ‘latent space’ through a series of analogies, and then briefly situates the concept in relation to a genealogy reaching back to developments in mathematical statistics at the turn to the 20th century.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"374 - 379"},"PeriodicalIF":1.7,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45141099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-10DOI: 10.1177/14780771231168224
Nemeh Rihani
This paper investigates the potential of dense multi-image 3D photogrammetric reconstruction of destroyed cultural heritage monuments by employing public domain imagery for heritage site visitors. This work focuses on the digital reconstruction of the Temple of Bel, one of the heritage monuments in Palmyra, Syria, which was demolished in the summer of 2015 due to armed conflict. This temple is believed to be one of the most significant religious structures of the first century AD in the Middle East and North Africa (MENA) region with its unique design and condition before destruction actions. The process is carried out using solely one source of images; the freely available visitors’ images collected from the social media platforms and web search engines. This paper presents a digital 3D reconstruction workflow for the collected images using an advanced photogrammetry pipeline and dense image matching software. The virtually reconstructed outputs will be managed and implemented efficiently in Unity3D to create an entire 3D virtual interactive environment for the deconstructed temple to be visualised and experienced using the new Oculus Quest VR headset. The virtual Palmyra’s visitor will be offered an enhanced walk-through off-site interactive, immersive experience compared to the real-world one, which is non-existing and unobtainable at the site in the current time.
{"title":"Interactive immersive experience: Digital technologies for reconstruction and experiencing temple of Bel using crowdsourced images and 3D photogrammetric processes","authors":"Nemeh Rihani","doi":"10.1177/14780771231168224","DOIUrl":"https://doi.org/10.1177/14780771231168224","url":null,"abstract":"This paper investigates the potential of dense multi-image 3D photogrammetric reconstruction of destroyed cultural heritage monuments by employing public domain imagery for heritage site visitors. This work focuses on the digital reconstruction of the Temple of Bel, one of the heritage monuments in Palmyra, Syria, which was demolished in the summer of 2015 due to armed conflict. This temple is believed to be one of the most significant religious structures of the first century AD in the Middle East and North Africa (MENA) region with its unique design and condition before destruction actions. The process is carried out using solely one source of images; the freely available visitors’ images collected from the social media platforms and web search engines. This paper presents a digital 3D reconstruction workflow for the collected images using an advanced photogrammetry pipeline and dense image matching software. The virtually reconstructed outputs will be managed and implemented efficiently in Unity3D to create an entire 3D virtual interactive environment for the deconstructed temple to be visualised and experienced using the new Oculus Quest VR headset. The virtual Palmyra’s visitor will be offered an enhanced walk-through off-site interactive, immersive experience compared to the real-world one, which is non-existing and unobtainable at the site in the current time.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2023-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43811785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-04-03DOI: 10.1177/14780771231168233
Xinwei Zhuang, Yi Ju, Allen Yang, Luisa Caldas
Generative design in architecture has long been studied, yet most algorithms are parameter-based and require explicit rules, and the design solutions are heavily experience-based. In the absence of a real understanding of the generation process of designing architecture and consensus evaluation matrices, empirical knowledge may be difficult to apply to similar projects or deliver to the next generation. We propose a workflow in the early design phase to synthesize and generate building morphology with artificial neural networks. Using 3D building models from the financial district of New York City as a case study, this research shows that neural networks can capture the implicit features and styles of the input dataset and create a population of design solutions that are coherent with the styles. We constructed our database using two different data representation formats, voxel matrix and signed distance function, to investigate the effect of shape representations on the performance of the generation of building shapes. A generative adversarial neural network and an auto decoder were used to generate the volume. Our study establishes the use of implicit learning to inform the design solution. Results show that both networks can grasp the implicit building forms and generate them with a similar style to the input data, between which the auto decoder with signed distance function representation provides the highest resolution results.
{"title":"Synthesis and generation for 3D architecture volume with generative modeling","authors":"Xinwei Zhuang, Yi Ju, Allen Yang, Luisa Caldas","doi":"10.1177/14780771231168233","DOIUrl":"https://doi.org/10.1177/14780771231168233","url":null,"abstract":"Generative design in architecture has long been studied, yet most algorithms are parameter-based and require explicit rules, and the design solutions are heavily experience-based. In the absence of a real understanding of the generation process of designing architecture and consensus evaluation matrices, empirical knowledge may be difficult to apply to similar projects or deliver to the next generation. We propose a workflow in the early design phase to synthesize and generate building morphology with artificial neural networks. Using 3D building models from the financial district of New York City as a case study, this research shows that neural networks can capture the implicit features and styles of the input dataset and create a population of design solutions that are coherent with the styles. We constructed our database using two different data representation formats, voxel matrix and signed distance function, to investigate the effect of shape representations on the performance of the generation of building shapes. A generative adversarial neural network and an auto decoder were used to generate the volume. Our study establishes the use of implicit learning to inform the design solution. Results show that both networks can grasp the implicit building forms and generate them with a similar style to the input data, between which the auto decoder with signed distance function representation provides the highest resolution results.","PeriodicalId":45139,"journal":{"name":"International Journal of Architectural Computing","volume":"21 1","pages":"297 - 314"},"PeriodicalIF":1.7,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49661083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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